The present invention relates to systems that include mechanisms operable to receive information and to analyze that information on the basis of a learning mode of operation.
Pattern recognition includes the ability of a circuit to detect a pattern among variables despite the fact that the pattern is not precisely the same pattern as was previously learned. The variables can be considered as any variable or set of variables from which a signal can be formed, in some way functionally related to the variables considered. The types of variables fall into two broad categories: static variables and time-varying variables. For example, when a color-blind person tries to distinguish between letters or numerals of pastel dots, he is given static variables or static information. Time-varying variables for which patterns might be recognized include audio signals, for example a person trying to distinguish between the dash and dot patterns he hears in a Morse code signal.
Clearly living organisms can accomplish this task of pattern recognition. People can recognize static information such as printed material (as the reader of these very words is now doing) and time-varying information such as how to swing a tennis racket so as to make proper contact with a tennis ball. Lower life forms also have this ability: certain ant species can recognize the foliage cover near their nests to orient themselves; certain moths can recognize the high-pitched sounds of a bat to avoid being captured; and even clams can learn primitive patterns of tactile responses which distinguish food from danger. Living organisms use electrochemical signals in the neurons of their brain or ganglion to perform this pattern recognition function.
While very complicated computers have been built which can do enormous numbers of calculations at speeds far exceeding the simple calculations done by house flies and clams, the ability of such computers to perform pattern recognition at the level of these primitive organisms has not been forthcoming. A major difference is that people tell the computers what to do whereas flies and clams tell themselves what to do. The former are essentially preprogrammed to do certain sequences in attempts to recognize patterns in space or in time while the latter self-organize themselves to "learn" to recognize patterns which are important to them. In each case, a certain amount of information is already known: in the computer it is a programming language (software) plus the myriad of interconnections in its circuitry; in the living organism it is its instincts or programmed patterns plus the myriad of interconnections in its neural circuitry.
Circuits made of wires and transistors and other electronic components could do well to have the ability to self-organize or learn as living organisms do. These circuits could lead directly to a machine which recognizes speech or recognizes handwriting among other tasks. A misconception is that people think but computers do not think--computers do only what people tell them to; however, self-organizing circuits of the type herein described mimic the ability of the brain to think or at least to do a major subtask of thinking which is pattern recognition. Hence, the line between a computer thinking and a person thinking becomes a fuzzy one.
To place the present invention in context, examples of the types of systems within which the concepts disclosed herein can be employed include the systems in U.S. Pat. Nos. 3,675,203 (Baumann); 4,040,011 (Crane et al.); 4,200,921 and 4,095,475 (Buckley, the present inventor).